Identifying New Directions in Database Performance Tuning

Database performance tuning is a complex and varied active research topic. With enterprise relational database management systems still reliant on the same set-based relational concepts that defined early data management products, the disparity between the object-oriented application development model and the object-relational database model, called the object-relational impedance mismatch problem, is addressed by techniques such as object-relational mapping (ORM). However, this has resulted in generally poor query performance for SQL developed by object applications and an irregular fit with cost-based optimisation algorithms, and leads to questions about the need for the relational model to better adapt to ORM-generated queries. This paper discusses database performance optimisation developments and seeks to demonstrate that current database performance tuning approaches need re-examination. Proposals for further work include exploring concepts such as dynamic schema redefinition; query analysis and optimisation modelling driven by machine learning; and augmentation or replacement of the cost-based optimiser model

Distinct encoded records join operator for distributed query processing

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2012Includes bibliographical references (leaves: 41-43)Text in English; Abstract: Turkish and Englishix, 49 leavesNowadays distributing data among different locations is very popular due to needs of business environment. In today's business environment, accessible, reliable, and scalable data is a critical need and distributed database system provides those advantages. It is a need to transfer data between sites while processing query in distributed database system, if the connection speed between sites is low then transmitting data is very time consuming. Optimizing distributed query processing is different from optimizing query processing in local database system. Most of the algorithms generated for distributed query processing focus on reducing the amount of data transferred between sites. Join operation in database system is for combining different tables with a common join attribute value, if the tables that are put in a join operation are at different locations then some of the tables are needed to be transferred to between sites. Join operation optimization algorithms in distributed database system focus on reducing the amount of data transfer by eliminating redundant tuples from relation before transmitting it to the other site. This thesis introduces a new distributed query processing technique named distinct encoded records join operation (DERjoin) which considers duplicated join attributes in a relation and eliminates them before sending the relation to another site

Reduction of collisions in Bloom filters during distributed query optimization.

The goal of distributed query optimization is to find the optimal strategy for the execution of a given query. The approaches in distributed query processing have mainly focused on the use of joins, semijoins, and filters. Semijoins have the advantage over joins in that there are no increases in data sizes. However, a semijoin needs more local processing such as projection and higher data transmission. To improve the distributed query processing, the filter-based approach is utilized. One of the limitations of this approach is collisions. We investigate how collisions affect the performance of the algorithm and how performance can be improved given those collisions. Our proposed algorithm utilizes two sets of filters to reduce the collisions, so the performance has been improved when collisions exist. Our proposed algorithm is evaluated objectively by comparison to a full reducer which is the algorithm that fully reduces all relations involved in a query by eliminating all non-participating tuples from the relations. The results of the evaluation show that: (1) With a perfect hash function, on average, our algorithm eliminates 97.41% of the unneeded data and fully reduces the relations of over 70% of the queries. (2) Using a single set of filters with specific percentages of collisions, on average, less than half of a queries are fully reduced by the algorithm. Therefore, the collisions substantially affects the performance. (3) Using two sets of filters, On average, our algorithm eliminates 95% of noncontributive tuples and achieves over 60% full reduction. In conclusion, our improved algorithm utilizes the two sets of filters to reduce the effects of collisions substantially. Therefore, we improve the performance of our algorithm under the assumption of collisions which is the major problem in using Bloom filters during distributed query optimization.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1999 .L53. Source: Masters Abstracts International, Volume: 39-02, page: 0528. Adviser: Joan Morrissey. Thesis (M.Sc.)--University of Windsor (Canada), 1999